{"ID":2848246,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00108","arxiv_id":"2511.00108","title":"Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence","abstract":"This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source embodied multimodal brain model. Its core advantage lies in the in-depth integration of data power and intelligent adaptive learning mechanisms. Specifically, metaloop distilled a high-quality dataset from a raw dataset containing 4+ billion tokens. Pelican-VL 1.0 is trained on a large-scale cluster of 1000+ A800 GPUs, consuming over 50k+ A800 GPU-hours per checkpoint. This translates to a 20.3% performance uplift from its base model and outperforms 100B-level open-source counterparts by 10.6%, placing it on par with leading proprietary systems on well-known embodied benchmarks. We establish a novel framework, DPPO (Deliberate Practice Policy Optimization), inspired by human metacognition to train Pelican-VL 1.0. We operationalize this as a metaloop that teaches the AI to practice deliberately, which is a RL-Refine-Diagnose-SFT loop.","short_abstract":"This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source embodied multim...","url_abs":"https://arxiv.org/abs/2511.00108","url_pdf":"https://arxiv.org/pdf/2511.00108v2","authors":"[\"Yi Zhang\",\"Che Liu\",\"Xiancong Ren\",\"Hanchu Ni\",\"Shuai Zhang\",\"Zeyuan Ding\",\"Jiayu Hu\",\"Hanzhe Shan\",\"Zhenwei Niu\",\"Zhaoyang Liu\",\"Shuang Liu\",\"Yue Zhao\",\"Junbo Qi\",\"Qinfan Zhang\",\"Dengjie Li\",\"Yidong Wang\",\"Jiachen Luo\",\"Yong Dai\",\"Zenglin Xu\",\"Bin Shen\",\"Qifan Wang\",\"Jian Tang\",\"Xiaozhu Ju\"]","published":"2025-10-30T19:55:13Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.RO\"]","methods":"[]","has_code":false}